How to Create a Python ETL Task in Azure Functions with Blob Storage Integration?

Aadhil Imam 90 Reputation points


I am working on an ETL pipeline using Azure Functions, and I need some guidance on the best approach and implementation details. Here’s what I am trying to achieve:

  1. Trigger: I want to use an appropriate trigger to run my Azure Function whenever a new file is added to an Azure Blob Storage container.
  2. Processing: The function should read the data from the source blob storage, process the data using Python, and then write the processed data to a destination blob storage.


  1. Trigger Selection: Which trigger is best suited for this scenario? I have heard about Blob triggers, but I am not sure if there are other triggers that might be more appropriate for this use case.
  2. Implementation: Could someone provide a step-by-step guide or point me to relevant documentation on how to set this up?
  3. Code Example: If possible, can you provide a simple Python code example demonstrating the read-process-write flow using Azure Functions and Blob Storage?

Thank you in advance for your help!

Azure Functions
Azure Functions
An Azure service that provides an event-driven serverless compute platform.
4,504 questions
Azure Blob Storage
Azure Blob Storage
An Azure service that stores unstructured data in the cloud as blobs.
2,556 questions
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Accepted answer
  1. Vlad Costa 615 Reputation points

    Hi @Aadhil Imam

    The Blob Storage trigger would be the most appropriate for your scenario. This trigger starts a function whenever a new or updated blob is detected in the specified Blob Storage container.

    For the implementation, you can follow these general steps:

    1. Create an event-based Blob Storage triggered function in a new project.
    2. Validate locally within Visual Studio Code using the Azurite emulator.
    3. Create a blob storage container in a new storage account in Azure.
    4. Create a function app in the Flex Consumption plan (preview).
    5. Create an event subscription to the new blob container.
    6. Deploy and validate your function code in Azure.

    For a Python code example, you can refer to the following snippet:

    import logging
    import azure.functions as func
    from import BlobServiceClient
    def main(myblob: func.InputStream):"Python blob trigger function processed blob \n"
                     f"Name: {}\n"
                     f"Blob Size: {myblob.length} bytes")
    	# Create a blob service client
        blob_service_client = BlobServiceClient.from_connection_string("<your_connection_string>")
    	# Get the container client
        container_client = blob_service_client.get_container_client("<your_container_name>")
    	# Read the blob data
        blob_client = container_client.get_blob_client(
        data = blob_client.download_blob().readall()
    	# Process the data
        processed_data = process_data(data)  # Replace with your data processing logic
    	# Write the processed data to the destination blob
        dest_blob_client = container_client.get_blob_client("<destination_blob_name>")
        dest_blob_client.upload_blob(processed_data, overwrite=True)

    Please replace <your_connection_string>, <your_container_name>, and <destination_blob_name> with your actual Blob Storage connection string, container name, and destination blob name, respectively. Also, replace process_data(data) with your actual data processing logic.

    Additional reference:

    If this answer solves your problem, please mark it as accepted.

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